Multi parametric classification of cardiovascular sounds

ABSTRACT

The present application relates to a method for classifying a cardiovascular sound recorded from a living subject. The method comprises the step of extracting at least two signal parameters ( 309 ) from said cardiovascular sound, said at least two signal parameters characterizes at least two different properties of at least a part of said cardiovascular sound. The method further comprises the step of classifying said cardiovascular sound using said at least two signal parameters in a multivariate classification method ( 310 ). Furthermore, the application relates to a system, stethoscope and server for classifying a cardiovascular sound recorded from a living subject, where the above-described method has been implemented.

FIELD OF THE INVENTION

The present invention relates to methods and systems for classificationof heart sounds recorded from a living subject into classes describingwhether or not murmurs due to coronary artery stenosis is present in theheart sound.

BACKGROUND OF THE INVENTION

Coronary artery disease is the single most common cause of death fromcardiovascular disease in the western world. The heart muscle receivesits blood supply through the coronary arteries, and atherosclerosis isthe most common pathophysiologic process occurring in the coronaryarteries giving rise to coronary artery disease (CAD). Atherosclerosisis a process that builds up plaques within the artery, and the bloodflow can therefore be is reduced or even blocked by the plaque. Theconstantly working heart requires a continuous and efficient bloodsupply in order to work properly. Defects in the blood supply may bevery severe and even fatal. Increasing degrees of luminal diameterreduction or stenosis of the coronary artery will first limit reserveflow, then reduce flow at rest and may finally totally occlude thevessel.

There is a need for measuring/detecting coronary artery stenosis forclinicians and other medical professionals to diagnose CAD. Once adiagnose has been made a cure/treatment could be started.

Today several non-invasive techniques for measuring/detecting theseverity of a stenosis or its presence inside a coronary artery exist.This can be done by magnetic resonance imaging (MRI), in vivointravascular ultrasound (IVUS) or optical coherence tomography (OCT).However, the above-mentioned techniques are all rather complicated andexpensive to use and therefore only patients with specific symptoms areoffered such examinations. The consequence is that most patients have acritical stenosis when examined.

Clinicians and other medical professionals have long relied onauscultatory sounds to aid in the detection and diagnosis ofphysiological conditions. For instance, a clinician may utilize astethoscope to monitor and record heart sounds in order to detect heartvalve diseases. Furthermore, the recorded heart sounds could bedigitized, saved and stored as data files for later analysis. Deviceshave been developed that apply algorithms to electronically recordedauscultatory sounds. One example is an automated blood-pressuremonitoring device. Other examples include analysis systems that attemptto automatically detect physiological conditions based on the analysisof auscultatory sounds. For instance, artificial neural networks havebeen discussed as one possible mechanism for analyzing auscultatorysounds and providing an automated diagnosis or suggested diagnosis.Using these conventional techniques, it is difficult to provide anautomated device for diagnosis of coronary stenosis using auscultatorysounds. Moreover, it is often difficult to implement the conventionaltechniques in a manner that may be applied in real-time or pseudoreal-time to aid the clinician.

OBJECT AND SUMMARY OF THE INVENTION

The object of the present invention is to solve the above-mentionedproblems.

This is achieved by a method for classifying a cardiovascular soundrecorded from a living subject. The method comprises the step ofextracting at least two signal parameters from said cardiovascularsound, said at least two signal parameters characterize at least twodifferent properties of at least a part of said cardiovascular sound.The method further comprises the step of classifying said cardiovascularsound using said at least two signal parameters in a multivariateclassification method.

Hereby a simple method for classifying cardiovascular sounds is achievedand the method is furthermore very robust since different properties ofthe cardiovascular sound is taken into account and used in amultivariate classification method. The cardiovascular sound related toturbulence consists of at least two components: a broad band componentcaused by turbulent blood flow colliding with the arterial wall and anarrow banded component related to the resonance frequency of the arterywall, therefore different variables describing different properties areneeded in order to perform a robust classification. The differentproperties describe different characteristics of the cardiovascularsound and would therefore be uncorrelated and therefore providedifferent information of the cardiovascular sound. Different propertiescould for instance be the time duration of the diastolic segment ofcardiovascular sound, the time duration of the systolic cardiovascularsound, the most dominant frequency component of the sound, the bandwidthof different frequency components, the energy in two frequency bands,the mobility of part of the signal, the complexity of the signal, thepower ratio between different parts of the signal, e.g. two differentsegments or two different frequency bands, morphological characteristicssuch as correlation ratios between different segments or amplitudechange over time. The method could easily be implemented in any kind ofdata processor unit and therefore be e.g. integrated in a softwareprogram which clinicians and doctors could use in order to classify thecardiovascular sound. Furthermore, the method could be integrated in adigital stethoscope and the stethoscope could therefore be used in orderto classify a patient's cardiovascular sound. Since doctors and otherclinicians are familiar with a stethoscope, they could easily be taughtto use the stethoscope to classify the cardiovascular sound. The resultis that the classification could assist the doctor or other cliniciansto diagnose whether or not the patient suffers from CAD.

In another embodiment of the method, at least one of said at least twosignal parameters is a frequency parameter describing a property in thefrequency domain of at least a part of said cardiovascular sound. Herebythe frequency components of the cardiovascular sound could be used as aparameter in the multivariable classification method. Frequencyparameters are very good parameters for classifying whether or notmurmurs due to stenosis are present in a cardiovascular sound becausethe stenosis would change the frequency components of the cardiovascularsound.

In another embodiment of the method, at least one of said at least twosignal parameters describes a property in the time domain of at least apart of said cardiovascular sound. Hereby time properties of thecardiovascular sound could be used as a parameter in the multivariableclassification method. Time properties like the mobility or number ofturning points are good indicators, whether or not murmurs due tostenosis are present in cardiovascular sound. Furthermore, by using bothtime and frequency parameters a very robust classification of thecardiovascular is achieved since time and frequency properties are oftenuncorrelated.

In another embodiment of the method, at least one of said frequency isparameters is a frequency level parameter describing a frequency levelproperty of at least a part of said cardiovascular sound. Hereby it isachieved that a frequency level property of the cardiovascular sound isused in the multivariable classification method. The murmurs wouldtypically change the frequency level of the cardiovascular sound, and byusing parameters describing the frequency level of the sound a robustclassification of the cardiovascular sound could be achieved.

In another embodiment of the method, at least one of said at least twosignal parameters is a frequency bandwidth parameter describing afrequency bandwidth property of at least a part of said cardiovascularsound. Hereby the bandwidth of, for instance, dominating frequencycomponents could be used in the multivariable classification method. Theadvantage of using a frequency bandwidth property of the cardiovascularsound is that murmurs often has a limited frequency bandwidth, and thefrequency bandwidth parameter would therefore be a good indicator ofwhether or not murmurs due to stenosis are present in the cardiovascularsound.

In another embodiment of the method, at least one of said frequencylevel properties characterizes the most powerful frequency component ofat least a part of said cardiovascular sound. This parameter is a veryuseful parameter as the murmurs due to stenosis typically have adominating frequency component between 200-800 Hz. And if the mostpowerful frequency component is inside this interval, it would be a goodindication of the presence of murmurs due to stenosis.

In another embodiment of the method, at least one of said frequencybandwidth properties characterizes the bandwidth of the most powerfulfrequency component of at least a part of said cardiovascular sound.Hereby the bandwidth of the most powerful frequency component could beused in the multivariable classification method. This bandwidth wouldmost likely depend on whether or not murmurs due to stenosis are presentin the cardiovascular sound.

In another embodiment of the method, at least one of said timeparameters is a property characterizing the mobility of at least a partof said cardiovascular sound. The mobility is a good indicator ofwhether or not murmurs due to stenosis are present in the cardiovascularsound. The mobility describes the variance of the sound, and sincemurmurs would cause larger variance in the sound the mobility would be agood indicator.

In another embodiment of the method, the method further comprises thestep of dividing said cardiovascular sound into at least one sub-segmentand at least one of said signal parameters is extracted from said atleast one sub-segment. Hereby it is achieved that the cardiovascularsound could be divided into sub-segments, e.g. into a systolic part anda diastolic part. Thereby relevant sub-segments could be used to extractthe above-described different parameters.

In another embodiment of the method, the method further comprises thestep of modelling at least a part of said cardiovascular sound and atleast one of said signal parameters is extracted from said model. Herebytime models and frequency models of the cardiovascular sound orsub-segments of the sound could e.g. be used to extract theabove-described parameters. The advantage of using models is that themodels could enhance the signal properties, e.g. by using an envelopefunction or an autoregressive model. Furthermore, models would simplifyand optimize the calculation process when the method is implemented in adata processor.

In another embodiment of the method, the multivariate classificationmethod is a discriminant function. Hereby a simple and fastimplementation of the classification method is achieved. Furthermore,any number of parameters could be used in the discriminant function, andthe different parameters could also be weighted differently depending onthe parameters' significance. The discriminant function could also betrained using cardiovascular test sounds is recorded from patientssuffering from stenosis and healthy patients. Thereby the weights of thedifferent parameters could be optimized to experimental data.

The invention further relates to a system for classifying acardiovascular sound recorded from a living subject, said systemcomprises processing means for extracting at least two signal parametersfrom said cardiovascular sound, said at least two signal parameterscharacterizes at least two different properties of at least a part ofsaid cardiovascular sound; processing means for classifying saidcardiovascular sound using said at least two signal parameters using amultivariate classification method. Hereby a system for classifying acardiovascular sound can be constructed and hereby the same advantagesas described above are achieved.

In a further embodiment of the system, said processing means forextracting at least two signal parameters from said cardiovascular soundis adapted to extract at least one frequency parameter describing aproperty in the frequency domain of at least a part of saidcardiovascular sound. Hereby the same advantages as described above areachieved.

In a further embodiment of the system, said processing means forextracting at least two signal parameters from said cardiovascular soundis adapted to extract at least one time parameter describing a propertyin the time domain of at least a part of said cardiovascular sound.Hereby the same advantages as described above are achieved.

In a further embodiment of the system, said processing means adapted toextract at least one of said frequency parameters are further adapted toextract at least one frequency level parameter describing a frequencylevel property of at least a part of said cardiovascular sound. Herebythe same advantages as described above are achieved.

In a further embodiment of the system, said processing means adapted toextract at least one frequency parameter is further adapted to extractat least one frequency bandwidth parameter describing a frequencybandwidth property of at least a part of said cardiovascular sound.Hereby the same advantages as described above are achieved.

In a further embodiment of the system, said processing means adapted toextract at least one frequency level property is further adapted toextract the most powerful frequency component of at least a part of saidcardiovascular sound. Hereby the same advantages as described above areachieved.

In a further embodiment of the system, said processing means adapted toextract at least one of said frequency bandwidth properties are furtheradapted to extract the bandwidth of the most powerful frequencycomponent of at least a part of said cardiovascular sound. Hereby thesame advantages as described above are achieved.

In a further embodiment of the system, said processing means forextracting at least one time parameter are further adapted to extractthe mobility of at least a part of said cardiovascular sound. Hereby thesame advantages as described above are achieved.

In a further embodiment of the system, said system further comprisesprocessing means for dividing said cardiovascular sound into at leastone sub-segment and at least one of said signal parameters is extractedfrom said at least one sub-segment. Hereby the same advantages asdescribed above are achieved.

In a further embodiment of the system, said system further comprisesprocessing means for modelling at least a part of said cardiovascularsound and in that said processing means for extracting at least twosignal parameters from said cardiovascular sound are further adapted toextract at least one of said parameters from said model. Hereby the sameadvantages as described above are achieved.

In a further embodiment of the system, said multivariate classificationmethod used by said processing means for classification of saidcardiovascular sound is a discriminant function. Hereby the sameadvantages as described above are achieved.

The invention further relates to a computer-readable medium havingstored therein instructions for causing a processing unit to execute amethod as described above. Hereby the same advantages as described aboveare achieved.

The invention further relates to a stethoscope comprising recordingmeans adapted to record a cardiovascular sound from a living subject,storing means adapted to store said recorded cardiovascular sound, acomputer-readable medium and a processing unit, said computer-readablemedium having stored therein instructions for causing said processingunit to execute a method according to claims 1-12 and thereby classifysaid recorded cardiovascular sound. Hereby the method according to thepresent invention can be implemented in a stethoscope and theabove-described advantages are achieved.

The invention further relates to a server device connected to acommunication network comprising receiving means adapted to receive acardiovascular sound recorded form a living subject through saidcommunication network, storing means adapted to store said receivedcardiovascular sound, a computer-readable medium and a processing unit,said computer-readable medium having stored therein instructions forcausing said processing unit to execute a method as described above andthereby classify said received cardiovascular sound. Hereby the methodaccording to the present invention can be implemented in a serverconnected to a communication network. The server could then perform theabove-described method and the above-described advantages are achieved.

In another embodiment of the server, said receiving means are furtheradapted to receive said cardiovascular sound from a client connected tosaid communication network. Hereby a clinician/doctor could send acardiovascular sound to the server using a client device such as alaptop. The server could thereafter classify the received cardiovascularsound. The above-described advantages are hereby achieved.

In another embodiment of the server, the server device further comprisesmeans for sending said classification of said cardiovascular sound to atleast one client unit connected to said communication network. Herebythe result of the classification can be sent back to a client, and theclinician/doctor can therefore receive the result of the classification.The above-described advantages are hereby achieved.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a graph of a typical heart sound,

FIG. 2 illustrates a fluid dynamic model of an arterial stenosis,

FIG. 3 illustrates an overview in form of a flow diagram of the methodaccording to the present invention,

FIG. 4 illustrates an embodiment of the system according to the presentinvention,

FIG. 5 illustrates another embodiment of the method according to thepresent invention,

FIG. 6 illustrates a flow diagram of the segmentation method,

FIG. 7 illustrates for a heart sound the relationship between theenvelope autocorrelation of a cardiac cycle and the cardiac cycle,

FIG. 8 illustrates the implementation of a Bayesian network used tocalculate the probability of a sound being an S1, S2 and noise sound.

DESCRIPTION OF EMBODIMENTS

FIG. 1 illustrates a graph of a typical heart sound recorded by astethoscope and shows the amplitude (A) of the sound pressure at they-axis and time (t) at the x-axis. The heart sounds reflect events inthe cardiac cycle: the deceleration of blood, turbulence of the bloodflow and the closing of valves. The closing of the valves is typicallyrepresented by two different heart sounds, the first (S1) and the second(S2) heart sound. The first and second heart sounds are illustrated inthe figure, and S1 marks the beginning of systole which is the part ofthe cardiac cycle in which the heart muscle contracts, forcing the bloodinto the main blood vessels, and the end of the diastole which is thepart of the heart cycle during which the heart muscle relaxes andexpands. During diastole, blood fills the heart chambers. The durationof systolic segments is nearly constant around 300 ms for healthysubjects. Given a pulse of 60 beats per minute the duration of a cardiaccycle will be one second on average, and the duration of the diastolewill be 700 ms. However, the diastolic durations are not constant, butwill vary depending on the subject's pulse. In addition, smallervariations of the diastolic duration are introduced due to neuralregulation and the effects of respiration.

FIG. 2 illustrates a fluid dynamic model of an arterial stenosis andshows an artery (201) with a stenotic lesion (202). The arrows (203)indicate the blood flow through the artery. Vortices (204) will occurwhen high velocity blood exits a stenotic lesion (202). These vorticescollide with the arterial wall (205) and are transformed into pressurevibrations that cause the arteries to vibrate at their resonancefrequencies. The result is that soundwaves in the form of murmers (206)with a frequency corresponding to the aterial wall's resonancefrequencies are created and emitted from the arterial wall. Resonancefrequencies in the arterial segment are increased if a stenosis ispresent and their frequencies depend on the diameter of the stenoticsegment compared to the diameter of the artery. As the severity of astenosis increases, so does the resonance frequency. The resonancefrequency of a partial occluded stenotic artery is most likely between200 Hz to 1100 Hz. The intensity of the vortice fluctuations depends onthe blood flow so that murmurs from the left coronary arteries are mostintense during diastole, when the blood flow through these arteries ishighest. Murmurs from the right coronary arteries are most intenseduring diastole if there is a stenosis in branches of the right coronaryartery supplying the right-sided cavities, whereas the murmur morelikely will be systolic from those branches of the right coronary arterygiving arterial blood to the left ventricle. The intensity of murmursnot only depends on the blood flow, but also on the frequency content ofa murmur. High murmur frequencies are more suppressed by the chest wallcompared to low frequencies. The murmurs caused by the arterialvibrations would affect the graph of a heart sound recorded by e.g. astethoscope.

FIG. 3 illustrates an overview in form of a flow diagram of the methodaccording to the present invention. The method could for instance beimplemented as a software program running on a computer or on amicrocontroller implemented in a stethoscope. In short, the methodstarts with an initialization (301), receiving a test signal (302),dividing the test signal into relevant segments (306), filtering therelevant segments (307); calculating/developing a model of the signal(308) in relevant segments; extracting different parameters from thesignal and the model (309), performing an analysis of the signal (310)using the extracted parameters and classifying the relevant segmentsinto two groups: one indicating that the signal contains murmurs due tostenosis (311), and one indicating that the signal does not containmurmurs due to stenosis (312).

After the method has been initialized (301) the method receives the testsignal (302) as a data file (303). The test signal would be the heartsound from a person (304) recorded and digitalized into a data file,e.g. by a digital stethoscope (305). The test signal would be similar tothe heart sound illustrated in FIG. 1, however, the duration of the testsignal would typically be 5-15 times longer than the signal shown inFIG. 1. Once the test signal has been received (302), segmentation (306)is performed in order to detect and divide the test signal intosegments. The segmentation process would typically detect the heartsounds S1 and S2 and thereafter divide the test signal into systolic anddiastolic parts. Hereafter the test signal is filtered (307), and thefiltration process includes an autoregressive filter that reduces whitenoise in the signal and a band pass filter that only lets frequenciesbetween 450-1100 Hz pass. The test signal would thereafter contain thefrequencies caused by the vibrations of the arterial wall when stenosisis present in the artery. The autoregressive filter could be implementedas a Kalman filter that is a powerful estimator of past, present andfuture states and it can do so even when the precise nature of themodelled system is unknown. This is a desirable feature in the presentapplication when reducing the effects of noise since the exactcomposition of a murmur is unknown. A first order Kalman filter canreduce the effects of white noise and smooth the noisy heart soundrecordings for further processing. The band pass filter could beimplemented as a wavelet filter. In another embodiment the Kalman filteris omitted in order to simplify the implementation of the method in e.g.a microprocessor and further to reduce the number of calculationsperformed by the microprocessor.

When the signal has been filtered (307), relevant segments are selectedfor further analysis. In one embodiment a part of the diastolic segmentis selected for further analysis as the murmur due to stenosis is mostlikely to be audible in the diastolic segment.

A mathematical model of the signal in the selected segment is hereaftercalculated/developed (308) using the sampled heart sound in the datafile. The model is used to extract parameters that characterize thesound in the segment and could be used to categorize whether or not themurmurs due to stenosis exist in the sound segment. In the presentembodiment an autoregressive all-pole parametric estimation (AR-model)is used to model the signal. In the AR-model the sampled sound signal,y, from the data file is modelled as a linear combination of M pastvalues of the signal and the present input, u, driving the soundgenerating process. The model can be described by the followingequation:

$\begin{matrix}{{y(n)} = {{- {\sum\limits_{p = 1}^{M}{a_{p}{y\left( {n - p} \right)}}}} + {u(n)}}} & \lbrack 3.1\rbrack\end{matrix}$

where M represents the model order, A_(p) the AR coefficients and n thesample number. The AR coefficients are determined through anautocorrelation and by minimizing the error associated with the model.

The AR model in this embodiment is used to extract frequency parametersdescribing the heart sound. A second order model M=2 is preferredbecause it makes a better separation between the frequency parametersextracted from a heart sound with murmurs present and the frequencyparameters extracted from a heart sound with murmurs present.

Thereafter different parameters are extracted (309) from the sampledsignal and the AR model using signal processing techniques. Someparameters could be extracted from the selected segments. Each parametercharacterize the heart sound in the selected segments and couldtherefore be used to categorize the heart sound, e.g. whether or notmurmurs due to stenosis are present in the heart sound. The parameterscan in this embodiment be the number of turnings points per signallength, TP; the mobility of the signal, MB; pole magnitude, PM;normalized AR-peak frequency, NF; and AR spectral ratio, SR.

The number of turning points TP is extracted from the sampled signal inthe time domain, and it is found by calculating the number of turns thesignal performs in the time domain per unit time. This could be done bydetermining the amount of local maxima in a time period. Thus:

$\begin{matrix}{{T\; P} = \frac{{number}\mspace{14mu} {of}\mspace{14mu} {turns}}{{signal}\mspace{14mu} {length}}} & \lbrack 3.2\rbrack\end{matrix}$

The mobility MB is extracted from the sampled signal in the time domainand found by calculating the variance, σ_(y), of the signal in the timedomain and the variance of the signal's first derivative, σ_(y′). Themobility is hereafter found by:

$\begin{matrix}{{M\; B} = {\frac{\sqrt{\sigma_{y^{\prime}}^{2}}}{\sqrt{\sigma_{y}^{2}}} = \frac{\sigma_{y^{\prime}}}{\sigma_{y}}}} & \lbrack 3.3\rbrack\end{matrix}$

The pole magnitude PM is found by transforming the AR-model into thez-domain and calculating the magnitude of the poles in z-domaindescribed by the AR-spectrum.

The normalized AR peak frequency NF is based on the assumption thatmurmurs due to stenosis are more likely to be found in the diastolicsegment than in the systolic segment. The NF is found by calculating theangle of the poles in the AR-spectrum in the z-plane and transformingthis into a frequency of both a diastolic segment and a systolicsegment. If the absolute difference between the two is less than 25 Hz,which is typical in cases where no murmurs due to stenosis are present,then 25 Hz is subtracted from the diastolic peak frequency. If theaverage diastolic frequency is more than 50 Hz greater than the averagesystolic peak frequency, which is typical when murmurs due to stenosisare present, then 25 Hz is added to the average peak diastolicfrequency.

The AR spectral ratio SR is found by calculating the ratio of the energyin the frequency rang 200-500 Hz to the energy in the frequency range500-1000 Hz of a diastolic segment.

The extracted parameters are thereafter used in a multiparametricdiscriminant function in order to classify whether or not the soundsegment contains murmurs due to stenosis (310). In this embodiment alinear discriminant function is used to classify the sound segments. Thelinear discriminant function combines weighted features into adiscriminant score g(x) and could be described by:

g(x)=w ₁ x ₁ +w ₂ x ₂ +w ₃ x ₃ + . . . +w _(k) x _(k) +w _(i0) =w ^(T)x+w ₀  [3.4]

where x is the feature vector consisting of the extracted parameters, krepresents the number of features, i represents the classes and w is aweight vector that holds the discriminant coefficients. In the casewhere only two classes must be separated, a single discriminant functionis used. A two class classifier is called a dichotomizer. A dichotomizernormally classifies the feature vectors with the decisions boarderg(x)=0 (due to the constant w₀). If the discriminant score g(x) isgreater than zero the segment is assigned to class 1, otherwise it isassigned to class 2. Since g is a linear function g(x)=0 it defines ahyperplane decision surface, dividing the multi dimensional space intotwo half sub spaces. The discriminant score g(x) is the algebraicdistance to the hyper-plane. The discriminant function needs to betrained in order to find the weights values, w, and make a safe androbust classification of the sound segments. The discriminant trainingprocedure needs to be performed before using the system, and the purposeof the procedure is to find the optimal weights values of w so that thehyper plane separates the feature vectors optimally. The trainingprocedure is in one embodiment carried out by using 18 test soundsrecorded from 18 test persons where nine test persons have coronarystenosis and nine test persons do not have coronary stenosis. Thediscriminant training procedure is performed by using the statisticalsoftware program SPSS v.12.0 for windows (SPSS inc., Chicago Ill., USA).The above-mentioned parameters are extracted from the 18 training soundsand used as statistical inputs to the software program. The resultingdiscriminant could be:

g(x)=164.709MB−0.061NF−78.027PM+27,188SR+91.878TP+33,712  [3.5]

where MB is the mobility of the signal, NF the AR-peak frequency, PM thepole magnitude, SR the AR spectral ratio and TP the number of turningpoint.

If the result of the discriminant function is larger than zero (g(x)>0)then the sound segment does not contain murmurs due to stenosis (312).On the other hand, if the discriminant function is smaller than zero(g(x)<0) then the sound segment contains murmurs due to stenosis (311).

The discriminant function could by a person skilled in the art easily beadjusted to include additional or fewer parameters in order to develop aproper discriminant function that can be used to classify the heartsound. Further parameters could for instance be:

The Complexity, CP, of the sampled signal in the time domain. Thisparameter is based on the ratio of the mobility of the first derivativeof the signal to the mobility of the signal itself where y″ is thesecond derivative of the filtered heart sound signal. The complexitymeasure is relatively sensitive to noisy signals since it is based onthe second derivative.

$\begin{matrix}{{C\; P} = {\frac{{MB}_{y^{\prime}}}{{MB}_{y}} = \frac{\sigma_{y^{''}}/\sigma_{y^{\prime}}}{\sigma_{y^{\prime}}/\sigma_{y}}}} & \lbrack 3.6\rbrack\end{matrix}$

Further, the AR-peak frequency (PF) could be extracted and used in thediscriminant function. The AR-peak frequency could be found bycalculating the angle of the AR poles in the z-plane.

The parameters used in the discriminant function could be extracted fromdifferent segments of the heart sound, e.g. a number of differentdiastolic segments where a number of parameters is extracted from eachdiastolic segment. Thereafter an average value of each parameter couldbe calculated and used as input in the discriminant function.

FIG. 4 a illustrates an embodiment of the system according to thepresent invention where a server (401) is programmed to execute themethod described in FIG. 3. Furthermore, the server is connected to anetwork (402), e.g. the Internet and adapted to on request to receiveand analyze heart sound. Clinicians or other medical professionals wouldrecord the heart sound from a patient by a digital stetoscope (305) andthereafter transmit the digitalized heart sound to a personal computer(403). The clinician can hereafter send a request to the server in orderto have the heart sound analyzed. Once the server has analyzed the heartsound the result is automatically sent back to the clinician. FIG. 4 billustrates a flow diagram of the process and the communication betweenthe personal computer (403) and the server. The left hand siderepresents the client side (410) and the right hand side represents theserver side (411). First the client sends a heart sound in digital formto the server (412). Thereafter the server performs the methodillustrated in FIG. 3 and sends (413) the result of the analysis back tothe client where it is displayed (414) to the clinician. The cliniciancould hereafter evaluate the result in order to choose the righttreatment of the patient.

The system according to the present invention could also be implementedas an all in one digital stethoscope. The stethoscope would thereforeautomatically perform the analysis described in FIG. 3 when a heartsound has been recorded. This means that the method described in FIG. 3needs to be implemented in stethoscopes' processing means, and theresult of the analysis could e.g. be displayed on a small LCD integratedin the stethoscope. An advantage of this embodiment is that mostclinicians are familiar with a digital stethoscope and could thereforeeasily learn to use the stethoscope to diagnose whether or not thepatient has a coronary stenosis.

FIG. 5 illustrates another embodiment of the method described in FIG. 3.When the signal has been filtered (307), relevant segments are selectedfor further analysis. In one embodiment a part of the diastolic segmentis selected for further analysis as the murmur due to stenosis is mostlikely to be audible in the diastolic segment. In this embodiment thediastolic segment comprising respiration sounds is discarded (501). Thisis done by calculating the energy level of the diastolic segment in thefrequency band 200-440 Hz and comparing this energy level with themedian energy level of the entire diastolic segment. The diastolicsegment would be discarded if the energy level of the 200-440 Hzfrequency band is a factor 1.1 larger than the energy level in theentire diastolic segment.

The remaining diastolic segments are hereafter divided into sub-segments(502) with a duration of 37.5 ms or 300 samples. This is done becausethe blood flow in the coronary artery is not constant during a diastole,and the murmurs due to stenosis would therefore not be constant.

The variance of the signal in all sub-segments is then calculated andthe sub-segments with a variance larger than 1.3 of the median varianceof all sub-segments are then discarded (503). Hereby sub-segmentscomprising high noise spikes are removed.

Thereafter (504) none stationary sub-segments are removed. This is doneby dividing the sub-segment into sub-sub-segments with a duration of3.75 ms or 30 samples and then calculate the variance of eachsub-sub-segments. Thereby an outline of the variance throughout thesub-segment is constructed. The variance of the outline is thencalculated and the sub-segment is removed if the variance of the outlineis larger than 1.

At this point a number of sub-segments have been discarded in order toremove noisy and none stationary sub-segments. This would typicallyresult in 30-50 sub-segments from a cardiovascular recording ofapproximately 10 seconds.

The remaining sub-segments are thereafter used in step (308) and (309)as described in FIG. 3 in order to extract parameters describingdifferent properties of the cardiovascular signal. Thereafter the medianof each parameter is calculated using the values of the parameter formeach sub-segment (505). The median of each parameter is thereafter usedin the multiparametric discriminant function as described in FIG. 3. Inthis embodiment the following parameters are used: the mobility, thepower-ratio and the pole-amplitude of a 3 pole in an AR model of order6.

FIG. 6 illustrates a flow diagram of the segmentation method (306)according to the present invention used to automatic divide a heartsound (601) into sub-segments. The heart sound (601) has been recordedby a stethoscope and the signal has been digitized in order to digitallyprocess the signal. The graph shows the amplitude (A) of the soundintensity as a function of time (t). The heart sounds reflect events inthe cardiac cycle; the deceleration of blood, turbulence of the bloodflow and the closing of valves. The closing of the valves is typicallyrepresented by two different heart sounds, the first (S1) and the second(S2) heart sound. The first and second heart sounds are illustrated inthe figure, and (S1) marks the beginning of systole, which is the partof cardiac cycle in which the heart muscle contracts, forcing the bloodinto the main blood vessels, and the end of the diastole which is thepart of the heart cycle during which the heart muscle relaxes andexpands. During diastole, blood fills the heart chambers.

The purpose of the segmentation method is to classify the recorded heartsound into systolic, diastolic and noise segments. The illustratedmethod includes steps of noise reduction (602) followed by envelopecreation (603). The noise reduction could be implemented as a high-passfiler followed by removal of high amplitude friction noise spikes due toexternal noise like movement of the stethoscope during recording andthereafter a low pass filter. The purpose of the envelope creation is toenhance the trend of the signal. The envelope is in this embodimentcreated by calculating the Shannon energy of the signal:

se(n)=x(n)²·log x(n)²

where x is the signal and se is the Shannon energy. The high amplitudecomponents in the signal are weighted higher than low amplitudecomponents when calculating the Shannon energy. The envelope (613) ofthe heart sound (601) calculated by using the Shannon energy is shown infigure (613), and it can be seen that the heart sounds S1 and S2 areenhanced.

In order to classify the detected sounds into systolic segments,diastolic segments and noise components based on interval durations oneither side of the heart sounds S1 and S2, it is necessary to know howlong the intervals between S1's and S2's are. Therefore, the durationsof the heart cycles (systolic and diastolic intervals) are extractedfrom an autocorrelation of the envelope (604). This process is describedin detail in FIG. 7.

Candidates S1's and S2's are then detected (605) using the timeintervals extracted above and a threshold (614) on the envelope (613).To reduce the number of detected noise spikes, a minimum requirement isapplied to the candidate segments, which effectively removes some of theerroneously detected noise spikes. In some recordings there is a bigdifference between the intensity of S1 and S2 sounds. This causes aproblem since some of the low intensity sounds may be missed by thethreshold. As a result the segmentation method performs a test formissing S1 and S2 sounds (606). If it can be determined that somesegments are missing, the threshold procedure is rerun (607) using lowerlocal thresholds.

Once the signal has been divided into segments as described aboveinterval parameters and frequency parameters for each segment are thenextracted (608). The parameters aid in the classification of the soundsinto systolic segments and diastolic segments.

The interval parameters are four Boolean parameters extracted for eachsound by comparing the time duration to the previous sound and to thenext sound with the time intervals extracted using the autocorrelation.The parameters are:

-   -   AfterDia: Is true if the sound is succeeded by a second sound        after a period corresponding to the duration of a diastole,    -   AfterSys: Is true if the sound is succeeded by a second sound        after a period corresponding to the duration of a systole,    -   BeforeDia: Is true if the sound follows a second sound after a        period corresponding to the duration of a diastole,    -   BeforeSys: Is true if the sound follows a second sound after a        period corresponding to the duration of a systole.

The frequency parameter divides the sounds into low frequency and highfrequency sounds by calculating the median frequency of the sound. Thisis useful information as the first heart sound is expected to be a lowfrequency sound and the second heart sound is expected to be a highfrequency sound.

The parameters are parsed into a Bayesian network where the probabilityof a segment being a S1, S2 and noise sound is computed (609). Thefigure illustrates a bar chart (615) of the probability calculated foreach sound in the heart signal (601). Each sound would typically haveone dominating probability indicating the type (S1, S2 or noise) of thesound. Thereby all sounds are classified into S1, S2 and noise sounds.However, the probability of the three types would in some cases be moreor less equal and in such cases it is not possible to classify the soundinto a S1, S2 or noise sound using the Bayesian network.

The probabilities are used in the last step (610) to divide and verifythe heart signal into systole and diastole segments. This is done byusing the position of the identified S1 and S2 sounds to mark thebeginning of a systolic and diastolic sound segment respectively

The final result of the method (611) is the beginnings and ends of allidentified systoles and diastoles. Therefore a “train” (616) ofalternating systoles (617) and diastoles (618) can be created. Once thesystoles and diastoles have been identified they can be used in furtherdata handling, e.g. to extract further parameters from these segmentsand thereafter use the parameters to classify the medical condition ofthe recorded heart sound.

FIG. 7 illustrates the relationship between the envelope autocorrelationand the cardiac cycle, and how the intervals between heart sounds S1 andS2 can be found from the autocorrelation.

FIG. 7 a illustrates the envelope autocorrelation with the normalizedautocorrelation at the y-axis (NA) and the displacement (m) of theshifted envelope at the x-axis.

FIG. 7 b illustrates the displacement (m1) when the shifted envelope(701) is displaced by the duration of the systole corresponding to theunshifted envelope (702). The y-axis shows the amplitude (A) of theenvelope and the x-axis the time (t). The S1's in the displaced envelopeare multiplied by the S2's in the unshifted envelopes resulting in thefirst peak (703) seen in the autocorrelation.

FIG. 7 c illustrates the displacement (m2) when the shifted envelope(701) is displaced by the duration of the diastole corresponding to theunshifted envelope (702). The displaced S2's are multiplied by the S1'sin the unshifted envelope resulting in the second peak (704) seen in theautocorrelation.

FIG. 6 b illustrates the displacement (m3) when the shifted envelope(701) is displaced by the duration of the cardiac cycle corresponding tothe unshifted envelope (702). The S1's in the displaced envelope aremultiplied by the S1's in the unshifted envelope, and the S2's in thedisplaced envelope are multiplied by the S2's in the unshifted envelope.When this occurs the dominating peak (705) in the autocorrelation isproduced.

The interval between the heart sounds could therefore be found bymeasuring the distance between the peaks in the autocorrelation asdescribed above.

FIG. 8 illustrates the implementation of the Bayesian network used tocalculate the probability of a sound of being an S1, S2 and noise soundin step (809). The basic concept in the Bayesian network is theconditional probability and the posterior probability. The conditionalprobability describes the probability of the event a given the event b.

P(a|b)=x _(c)  [8.1]

If the above equation describes the initial conditional probability, theposterior probability would be:

P(b|a)=x _(p)  [8.2]

According to Bayes' rule the relation between the posterior probabilityand the conditional probability is:

$\begin{matrix}{{P\left( {ba} \right)} = \frac{{P\left( {ab} \right)}{P(b)}}{P(a)}} & \lbrack 8.3\rbrack\end{matrix}$

where P(a) is the prior probability for the event a, and P(b) is theprior probability for the event b. Equation [8.3] only describes therelation between one parent and one child, but since the event a can bethe combination of several events {a₁, a₂ , , , a_(n)} the equation canbe expanded to:

$\begin{matrix}{{P\left( {{ba_{1}},a_{2},,,,,a_{n}} \right)} = \frac{{P\left( {a_{1},a_{2},,,,,{a_{n}b}} \right)}{P(b)}}{P\left( {a_{1},a_{2},,,,,a_{n}} \right)}} & \lbrack 8.4\rbrack\end{matrix}$

Since the goal is to find the probability for the different states of bwhen a₁ and a₂ are known, P(a₁, a₂ , , , a_(n)) is just a normalizingconstant k and [7.4] can be simplified to:

P(b|a ₁ ,a ₂ , , , a _(n))=k·P(a ₁ ,a ₂ , , , a _(n) |b)P(b)  [8.5]

If child events (a₁, a₂ . . . a_(n)) are conditionally independent,equation [8.5] can be generalized to:

$\begin{matrix}{{P\left( {{ba_{1}},a_{2},,,,,a_{n}} \right)} = {{k \cdot {P(b)}}{\prod\limits_{i = 1}^{N}{P\left( {ib} \right)}}}} & \lbrack 8.6\rbrack\end{matrix}$

where N is the number of known events a. Equation [8.6] is useful indetermining the probability of the event b if the states of all a eventsare known and if all a events are conditionally independent. A Bayesiannetwork based on equation [8.6] is called a naive Bayesian networkbecause it requires conditional independency of the children.

The task for the Bayesian network is to evaluate the type of eachdetected sound above the detection threshold. For each of these sounds,the posterior probability of being an S1 sound, an S2 sound or a noisecomponent is calculated and the Bayesian network is constructed usingone parent and five children. The parent is a sound above the envelopethreshold (801), and the children are the five parameters describedabove: Frequency (802), AfterSys (803), AfterDia (804), BeforeSys (805)and BeforeDia (806). When determining the posterior probability for thetype of a particular sound, the prior probability for the differentstates of a sound type P(S) and the conditional probabilities must beknown, i.e. the conditional probabilities that “AfterSys” is in a givenstate when S is a given type, P(AfterSys|S). This posterior probabilityrequires definition of P(S), P(AfterSys|S), P(AfterDia|S),P(BeforeSys|S), P(BeforeDia|S) and P(Frequency|S) before the equation[8.6] can be used to calculate the posterior probability of a soundbeing a particular type of sound.

The prior probability that a sound is an S1, S2 or a noise componentchanges between recordings. In the optimal recording, where no noisecomponents are detected, the prior probability for noise is zero,P(S_(=Noise))=0. If this is the case and an equal number of S1's andS2's are detected, the prior probability that the detected sound is anS1 is 50%, and similar for S2. Therefore, P(S_(=S1))=P(S_(=S2))=0.5 ifP(S_(=noise))=0. However, this optimal condition cannot be assumed forreal signals, and noise sounds would be detected. This will increase theprior probability that a given sound is noise.

The exact probability of a detected sound being noise, P(S_(=noise)) canbe defined if the number of detected noise sounds, N_(noise) and thetotal number of detected sounds, N_(sounds) are known. For instance, ifit is known that four noise sounds are detected, N_(noise)=4, and thetotal number of detected sounds is 20, the probability that the soundbeing examined is a noise sound is P(S_(=Noise))=4/20. However, in mostsignals N_(noise) is unknown and an estimate of N_(noise) is thereforenecessary, and this estimate can be based on already availableinformation since the duration of a heart cycle is known from theenvelope autocorrelation (804). The expected number of cardiac cycles inone recording can therefore be calculated by dividing the length of therecording with the length of the cardiac cycles. The number of S1's andS2's in a recording is therefore twice the number of cardiac cycles in arecording. The prior probability of the sound type would therefore be:

$\begin{matrix}{{P\left( S_{= {noise}} \right)} = \frac{N_{noise}}{N_{sound}}} & \lbrack 8.7\rbrack\end{matrix}$

and the prior probability that the detected sound is an S1 or S2:

$\begin{matrix}{{P\left( S_{= {s\; 1}} \right)} = {{P\left( S_{= {s\; 2}} \right)} = \frac{1-={P\left( S_{= {noise}} \right)}}{2}}} & \lbrack 8.8\rbrack\end{matrix}$

The conditional probability that an S1 is followed by an S2 sound afteran interval corresponding to the duration of a systole,P(AfterSys|S_(=S1)), depends on several factors. The S1 sounds willnormally be followed by S2 sounds after an interval of duration equal tothe systole. Deviations from this can also occur, e.g. when S1 is thelast sound in the recording, or if S2 is missing because it is notdetected by the threshold. It may also occur that a weak (belowthreshold) S2 is detected because noise occurs in the tolerance windowassociated with those sounds. The probability that “AfterSys” is falseif the sound is an S1 sound may thus be calculated as

P(AfterSys_(=false) |S_(=S1))=P(EndSound∪Singlesound),NoiseInWin)  [8.9]

where “EndSound” is an event describing that the sound is the last soundin the recording. “SingleSound” describes that S1 is not followed by S2as the next S2 sound is not detected due to sub-threshold amplitude.“NoiselnWin” describes noise occurrence in the window, where the S2sound was expected. The conditional probability that “AfterSys” is truegiven that the examined sound is an S1 sound is given by:

P(AfterSys_(=true) |S _(=S1))=1−P(AfterSys_(=false) |S _(=S1))  [8.10]

If the examined sound is an S2 sound it is not likely that any soundoccurs after an interval corresponding to the systolic duration sincethe next S1 sound will occur after the duration of the diastole. Anexception is if a noise sound occurs in the window P(NoiseInWin) or ifthe systole and diastole durations are equal. If the duration of thediastole is equal to the duration of the systole, the S1 sound whichfollows the S2 sound after the duration of a diastole occurs in both thesystole tolerance window and in the diastole tolerance window. This willhappen if the heart rate of the subject is high. The probability that asound occurs in both tolerance windows (overlap) is equal to the degreeof the overlap between the systole and diastole tolerance window. Thisprobability is termed P(Overlap). Therefore, the conditional probabilitythat a sound occurs in the window after systole duration if the examinedsound is an S2 sound is:

P(AfterSys_(=true) |S _(=S2))=P(Overlap∪NoiseInWin)  [8.11]

The conditional probability that a sound does not occur after a systoleduration, if the examined sound is an S2, is the opposite of theconditional probability that it does occur:

P(AfterSys_(=false) |S _(=S2))=1−P(AfterSys_(=true) |S _(=S2))  [8.12]

The conditional probability that a detected noise sound is followed byanother sound after the systole duration is based on the probabilitythat a sound of any kind is present in a segment with the length of theused tolerance window. This can be estimated from the ratio of thetolerance window length multiplied by the number of detected soundsminus one to recording length.

P(SoundInWin|S _(=S2))=1−P(AfterSys_(=true) |S _(=S2))  [8.12]

The conditional probability that a detected noise sound is followed byanother sound after the systole duration, P(AfterSys|S_(=noise)), isbased on the probability that a sound of any kind is present in asegment with the length of the used tolerance window. This can beestimated from the ratio of the tolerance window length multiplied bythe number of detected sounds minus one to recording length. Theconditional probability that a noise sound is followed by another soundafter a systole duration is therefore:

$\begin{matrix}\begin{matrix}{{P\left( {{AfterSys}_{= {true}}S_{= {noise}}} \right)} = {P({SoundInWin})}} \\{= \frac{\left( {N_{sound} - 1} \right) \cdot 2 \cdot {Sys}_{tol}}{RecLength}}\end{matrix} & \lbrack 8.13\rbrack\end{matrix}$

where N_(sound) is the number of sounds within the recording, Sys_(tot)is the duration of a systole and RecLength is the length of therecording. The conditional probability that a noise is not followed byanother sound after the systole interval is the opposite:

P(AfterSys_(=false) |S _(=noise))=1−P(SoundInWin)  [8.14]

The conditional probabilities for P(AfterDia|S), P(BeforeSys|S) andP(BeforeDia|S) are based on the same assumptions used to defineP(AfterSys|S). These conditional probabilities can be found in thetables below:

False True P(AfterSys|S) S1 P((EndSound ∪ SingleSound), NoiseInWin) 1 −P((EndSound ∪ SingleSound), NoiseInWin) S2 1 − P(Overlap ∪ NoiseInWin)P(Overlap ∪ NoiseInWin) Noise 1 − P(SoundInWin) P(SoundInWin)P(AfterDia|S) S1 1 − P(Overlap ∪ NoiseInWin) P(Overlap ∪ NoiseInWin) S2P((EndSound ∪ SingleSound), NoiseInWin 1 − P((EndSound ∪ SingleSound),NoiseInWin) Noise 1 − P(SoundInWin) P(SoundInWin) P(AfterSys|S) S1 1 −P(Overlap ∪ NoiseInWin) P(Overlap ∪ NoiseInWin) S2 P((EndSound ∪SingleSound), NoiseInWin 1 − P((EndSound ∪ SingleSound), NoiseInWin)Noise 1 − P(SoundInWin) P(SoundInWin) P(AfterDia|S) S1 P((EndSound ∪SingleSound), NoiseInWin) 1 − P((EndSound ∪ SingleSound), NoiseInWin) S21 − P(Overlap ∪ NoiseInWin) P(Overlap ∪ NoiseInWin) Noise 1 −P(SoundInWin) P(SoundInWin)

It has previously been found that the frequency parameter classified 86%of the S1 sounds as low frequent and 80% of the S2 sounds as highfrequent. 85% of all noise sounds were classified as high frequent. Thisinformation was used as the conditional probabilities between thefrequency parameter P(Frequency|S):

P(Frequency|S) Low High S1 0.86 0.14 S2 0.20 0.80 Noise 0.15 0.85

When all conditional probabilities are found, equation [8.6] is used bythe Bayesian network to calculate the posterior probabilities for alldetected sounds. This way, three probabilities are calculated for eachsound that reflect how likely it is that the current sound is a giventype.

It should be noted that the above-mentioned embodiments ratherillustrate than limit the invention, and that those skilled in the artwill be able to suggest many alternative embodiments without departingfrom the scope of the appended claims.

1. A method for classifying a cardiovascular sound recorded from aliving subject, said method comprises the steps of: extracting at leasttwo signal parameters from said cardiovascular sound, said at least twosignal parameters characterizes at least two different properties of atleast a part of said cardiovascular sound, classifying saidcardiovascular sound using said at least two signal parameters in amultivariate classification method.
 2. A method according to claim 1characterized in that at least one of said at least two signalparameters is a frequency parameter describing a property in thefrequency domain of at least a part of said cardiovascular sound.
 3. Amethod according to claim 1 characterized in that at least one of saidat least two signal parameters describing a property in the time domainof at least a part of said cardiovascular sound.
 4. A method accordingto claim 2 characterized in that at least one of said frequencyparameters is a frequency level parameter describing a frequency levelproperty of at least a part of said cardiovascular sound.
 5. A methodaccording to claim 2 characterized in that at least one of said at leasttwo signal parameters is a frequency bandwidth parameter describing afrequency bandwidth property of at least a part of said cardiovascularsound.
 6. A method according to claim 4 characterized in that at leastone of said frequency level properties characterizes the most powerfulfrequency component of at least a part of said cardiovascular sound. 7.A method according to claim 5 characterized in that at least one of saidfrequency bandwidth properties characterizes the bandwidth of the mostpowerful frequency component of at least a part of said cardiovascularsound.
 8. A method according to claim 3 characterized in that at leastone of said time parameters is a property characterizing the mobility ofat least a part of said cardiovascular sound.
 9. A method according toclaim 1 characterized in that said method further comprises the step ofdividing said cardiovascular sound into at least one sub-segment and atleast one of said signal parameters is extracted from said at least onesub-segment.
 10. A method according to claim 1 characterized in thatsaid method further comprises the step of modelling at least a part ofsaid cardiovascular sound and at least one of said signal parameters isextracted from said model.
 11. A method according to claim 1characterized in that said multivariate classification method is adiscriminant function.
 12. A system for classifying a cardiovascularsound recorded from a living subject, said system comprises: processingmeans for extracting at least two signal parameters from saidcardiovascular sound, said at least two signal parameters characterizesat least two different properties of at least a part of saidcardiovascular sound, processing means for classifying saidcardiovascular sound using said at least two signal parameters using amultivariate classification method.
 13. A system according to claim 12characterized in that said processing means for extracting at least twosignal parameters from said cardiovascular sound is adapted to extractat least one frequency parameter describing a property in the frequencydomain of at least a part of said cardiovascular sound.
 14. A systemaccording to claim 12 characterized in that said processing means forextracting at least two signal parameters from said cardiovascular soundis adapted to extract at least one time parameter describing a propertyin the time domain of at least a part of said cardiovascular sound. 15.A system according to claim 13 characterized in that said processingmeans adapted to extract at least one of said frequency parameters arefurther adapted to extract at least one frequency level parameterdescribing a frequency level property of at least a part of saidcardiovascular sound.
 16. A system according to claim 13 characterizedin that said processing means adapted to extract at least one frequencyparameter are further adapted to extract at least one frequencybandwidth parameter describing a frequency bandwidth property of atleast a part of said cardiovascular sound.
 17. A system according toclaim 13 characterized in that said processing means adapted to extractat least one frequency level property are further adapted to extract themost powerful frequency component of at least a part of saidcardiovascular sound.
 18. A system according to claim 13 characterizedin that said processing means adapted to extract at least one of saidfrequency bandwidth properties are further adapted to extract thebandwidth of the most powerful frequency component of at least a part ofsaid cardiovascular sound.
 19. A system according to claim 14characterized in that said processing means for extracting at least onetime parameters are further adapted to extract the mobility of at leasta part of said cardiovascular sound.
 20. A system according to claim 12characterized in that said system further comprises processing means fordividing said cardiovascular sound into at least one sub-segment and atleast one of said signal parameters is extracted from said at least onesub-segment.
 21. A system according to claim 12 characterized in thatsaid system further comprises processing means for modelling at least apart of said cardiovascular sound and in that said processing means forextracting at least two signal parameters from said cardiovascular soundare further adapted to extract at least one of said parameters from saidmodel.
 22. A system according to claim 12 characterized in that saidmultivariate classification method used by said processing means forclassification of said cardiovascular sound is a discriminant function.23. A computer-readable medium having stored therein instructions forcausing a processing unit to execute a method according to claim
 1. 24.A stethoscope comprising: recording means adapted to record acardiovascular sound from a living subject, storing means adapted tostore said recorded cardiovascular sound, a computer-readable medium anda processing unit, said computer-readable medium having stored thereininstructions for causing said processing unit to execute a methodaccording to claim 1 and thereby classify said recorded cardiovascularsound.
 25. A server device connected to a communication networkcomprising: receiving means adapted to receive a cardiovascular soundrecorded from a living subject through said communication network,storing means adapted to store said received cardiovascular sound, acomputer-readable medium and a processing unit, said computer-readablemedium having stored therein instructions for causing said processingunit to execute a method according to claim 1 and thereby classify saidreceived cardiovascular sound.
 26. A server device according to claim 25characterized in that said receiving means are further adapted toreceive said cardiovascular sound from a client connected to saidcommunication network.
 27. A server device according to claim 25characterized in that said server device further comprises means forsending said classification of said cardiovascular sound to at least oneclient unit connected to said communication network.